Monkeys can identify pictures from words

Humans learn and incorporate cross-modal associations between auditory and visual objects (e.g., between a spoken word and a picture) into language. However, whether nonhuman primates can learn cross-modal associations between words and pictures remains uncertain. We trained two rhesus macaques in a delayed cross-modal match-to-sample task to determine whether they could learn associations between sounds and pictures of different types. In each trial, the monkeys listened to a brief sound (e.g., a monkey vocalization or a human word), and retained information about the sound to match it with one of 2-4 pictures presented on a touchscreen after a 3-second delay. We found that the monkeys learned and performed proficiently in over a dozen associations. In addition, to test their ability to generalize, we exposed them to sounds uttered by different individuals. We found that their hit rate remained high but more variable, suggesting that they perceived the new sounds as equivalent, though not identical. We conclude that rhesus monkeys can learn cross-modal associations between objects of different types, retain information in working memory, and generalize the learned associations to new objects. These findings position rhesus monkeys as an ideal model for future research on the brain pathways of cross-modal associations between auditory and visual objects.

Authors

Institutions

Publication Details

Journal
PLoS ONE
Published
2025-02-12
DOI
https://doi.org/10.1371/journal.pone.0317183
Citations
1
Primary Topic
Multisensory perception and integration
Type
article
Field-Weighted Citation Impact
2.12

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Monkeys can identify pictures from words

Luis Lemus, Elizabeth Cabrera-Ruiz, Mario Treviño, Tonatiuh Figueroa et al.
1 citations
PLoS ONE
Multisensory perception and integration
2.12
article

Monkeys can identify pictures from words

Luis Lemus, Elizabeth Cabrera-Ruiz, Mario Treviño, Tonatiuh Figueroa, Javier Pérez-Orive, José Rafael Arrieta Vergara, Marlen Alva, Miguel Mata-Herrera
article en
1 citations

Abstract

Humans learn and incorporate cross-modal associations between auditory and visual objects (e.g., between a spoken word and a picture) into language. However, whether nonhuman primates can learn cross-modal associations between words and pictures remains uncertain. We trained two rhesus macaques in a delayed cross-modal match-to-sample task to determine whether they could learn associations between sounds and pictures of different types. In each trial, the monkeys listened to a brief sound (e.g., a monkey vocalization or a human word), and retained information about the sound to match it with one of 2-4 pictures presented on a touchscreen after a 3-second delay. We found that the monkeys learned and performed proficiently in over a dozen associations. In addition, to test their ability to generalize, we exposed them to sounds uttered by different individuals. We found that their hit rate remained high but more variable, suggesting that they perceived the new sounds as equivalent, though not identical. We conclude that rhesus monkeys can learn cross-modal associations between objects of different types, retain information in working memory, and generalize the learned associations to new objects. These findings position rhesus monkeys as an ideal model for future research on the brain pathways of cross-modal associations between auditory and visual objects.

PLoS ONEVol. 20(2)
Baylor College of Medicine (US), Universidad de Guadalajara (MX), Instituto Nacional de Rehabilitación (MX), Universidad Nacional Autónoma de México (MX)
Secretaría de Estado de Ciencia, Tecnología e Innovación, Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México
Quality Education
Openalex Percentile: Top 17%
Multisensory perception and integration
2.12
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.